Empowering Businesses with Big Data and Predictive Analysis

In today’s world, big data and predictive analytics have become buzz words. Big data and predictive analytics deal with working with large amounts of data by analysing them to predict future events. Many professionals are now doing some kind of an online analytics course to gain knowledge of this field which further indicates its popularity. As the world is getting more advanced technology-wise and as data speeds are getting faster, everyone is using smart devices to run their lives. All the devices like smartphones, smartwatches and other gadgets continuously gather data around them. For example, a smartwatch can carry valuable information about your health, the calories you burn every day, your pulse rate etc.

However, the challenge is that the data sets are getting increasingly larger and hence are more complicated to process. With all the overload of data around us, organizations understand that this data provides them heaps of knowledge about their customers and the industry. The data collated from various devices can be analysed using statistical models and predictive analytics. This will allow organizations to predict future trends and plan their strategies accordingly. There are many ways businesses can be empowered using big data and predictive analytics. Some of the ways are listed below –

Inventory Forecasting and Planning

One of the crucial aspects of running business operations is to successfully predict the sales forecasts for the coming sales period. Failure to do so can have a negative effect on the bottom line and either create unrest in the distribution line due to lack of product supply or lead to wastage due to unnecessary surplus. Predictive modelling can be used in this case to forecast how much sales the company will get in the next period by using past sales trends in the given period and other statistically relevant data points. This will help the company in deciding the amount of inventory to plan for the coming sales period and meet the demand of the market while at the same time ensure no loss happens due to extra units produced. In this way, the company can make maximum profit. An example of this could be a television production facility predicting the sales of their television sets in the next year. They would utilise data points such as the trend of television sales in the last few years, the size of their current market for which they are planning, the price of competitor television sets and expected competitor behaviour in the coming year.

Customer Service and Support

Keeping customers happy is one of the most important things for any organization. The company runs well if the customers are satisfied with its products or services. Organizations realise the importance of keeping their customers satisfied. However, as major companies serve millions of customers globally, continuing with age-old marketing practices with no backing of data is not a reliable method. By using big data and predictive modelling techniques companies can forecast customer behaviour over a period. Huge data sets of customer’s past behaviour, market trends, lifestyle, demographics, usage, spending pattern etc can be used to analyse and predict future customer trends and accordingly devise strategies for future product lines, marketing expansion, brand communication etc.

Predict Failure Rates for Future Products or Services

One of the critical applications of predictive analytics techniques is in predicting the probability of a new product’s or service’s failure. High-risk industries like banking and insurance, airlines etc find it extremely important to check the failure probability of their proposed product or service. Insurance companies use highly advanced predictive analytics to assess the probability of any product’s failure. Even while considering a potential customer for an insurance plan, the company runs a host of analytical tools on the personal details of the person and judge whether they should accept his application for that insurance plan or not. This is done to avoid any failure because it can lead to huge financial losses for the company. By having a model for estimation of success or failure rates, the company gets a fair idea of the market performance that the product or service will have. Accordingly, it can use the results of these techniques to help their decision making.

In the present market environment where there is a lot of clutter of various brands offering the same product and service it is difficult for a company to stand out with its offerings. They need to catch the attention of the consumer and ensure they repeatedly choose their product or service over other competitor brands. Not only is this expensive, but there are no second chances in the highly competitive market. Therefore, it is important to make sound business decisions. This is where big data and predictive analytics is helping businesses. It is a blend of tool and techniques that can assist businesses in predicting the future and measuring the risks and opportunities present before taking big decisions.